Can MiniCPM-V 2.6 8B run on Radeon RX 7700S 8GB?

YES — With Offload

A70Great

Estimated from fit model

MiniCPM-V 2.6 8B needs ~8.5 GB VRAM. Radeon RX 7700S 8GB has 8.0 GB. With Q4_K_M quantization, expect ~25 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 8.5 GB, 24.5 tok/s, Runs with offload (needs ~0.3 GB host RAM)

8.5 GB required8.0 GB available

106% VRAM needed

0.5 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.3 GB host RAM)

Decode

24.5 tok/s

TTFT

7899 ms

Safe context

Memory

8.5 GB / 8.0 GB

Offload

10%

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsMiniCPM-V 2.6 8B on Radeon RX 7700S 8GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 24.5 tok/s decode · 7.9s TTFT (warm) · 61 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	37.4 tok/s	2821 ms	2K
Coding	A	Runs with offload (needs ~0.3 GB host RAM)	24.5 tok/s	7899 ms	2K
Agentic Coding	F	Too heavy	15.9 tok/s	17731 ms	2K
Reasoning	A	Runs with offload (needs ~0.3 GB host RAM)	24.5 tok/s	9336 ms	2K
RAG	F	Too heavy	15.9 tok/s	22164 ms	2K

Quantization options

How MiniCPM-V 2.6 8B (8B params) fits at each quantization level on Radeon RX 7700S 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A84
Q3_K_S	3	3.9 GB	Low	A84
NVFP4	4	4.5 GB	Medium	A84
Q4_K_MBest for your GPU	4	4.9 GB	Medium	A83
Q5_K_M	5	5.8 GB	High	F0
Q6_K	6	6.6 GB	High	F0
Q8_0	8	8.6 GB	Very High	F0
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run MiniCPM-V 2.6 8B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "openbmb/MiniCPM-V-2_6" \
  --hf-file "MiniCPM-V-2_6-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your Radeon RX 7700S 8GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	A	17.8 tok/s

Frequently asked questions

Can Radeon RX 7700S 8GB run MiniCPM-V 2.6 8B?

Yes, Radeon RX 7700S 8GB can run MiniCPM-V 2.6 8B with a A grade (Runs with offload (needs ~0.3 GB host RAM)). Expected decode speed: 24.5 tok/s.

How much VRAM does MiniCPM-V 2.6 8B need?

MiniCPM-V 2.6 8B (8B parameters) requires approximately 8.5 GB of memory with Q4_K_M quantization.

What is the best quantization for MiniCPM-V 2.6 8B?

The recommended quantization for MiniCPM-V 2.6 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will MiniCPM-V 2.6 8B run at on Radeon RX 7700S 8GB?

On Radeon RX 7700S 8GB, MiniCPM-V 2.6 8B achieves approximately 24.5 tokens per second decode speed with a time-to-first-token of 7899ms using Q4_K_M quantization.

Can Radeon RX 7700S 8GB run MiniCPM-V 2.6 8B for coding?

For coding workloads, MiniCPM-V 2.6 8B on Radeon RX 7700S 8GB receives a A grade with 24.5 tok/s and 2K context.

What context window can MiniCPM-V 2.6 8B use on Radeon RX 7700S 8GB?

On Radeon RX 7700S 8GB, MiniCPM-V 2.6 8B can safely use up to 2K tokens of context. The model's official context limit is 2K, but available memory constrains the safe maximum.

What should I upgrade first if MiniCPM-V 2.6 8B feels slow on Radeon RX 7700S 8GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for Radeon RX 7700S 8GB See all hardware for MiniCPM-V 2.6 8B

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